Papers by Ruoxi Sun
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data (2023.findings-emnlp)
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| Challenge: | Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. |
| Approach: | They propose a method to improve few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs) they propose 'SQlPrompt' which aims to diversify the SQL proposals during consistency selection with different prompt designs and foundation models. |
| Outcome: | The proposed method outperforms previous approaches for in-context learning with zero labeled data by a large margin, closing the gap with finetuning state-of-the-art with thousands of labeles. |
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation (2024.naacl-long)
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| Challenge: | Large language models generate "hallucinated" answers that are not factual . despite their widespread adoption, they can generate plausiblesounding but nonfactual information. |
| Approach: | They propose a framework that tunes large language models to self-ground claims and provide citations to retrieved documents. |
| Outcome: | The proposed framework generates superior grounded responses with more accurate citations compared to prompting-based approaches and post-hoc citing-based methods. |
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)
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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun
| Challenge: | Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge. |
| Approach: | They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
| Outcome: | The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models (2025.acl-long)
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| Challenge: | Existing studies have not linked the behavior of retrieval augmented generation (RAG) with imperfect retrieval, including irrelevant, misleading, or even malicious information. |
| Approach: | They propose an approach that integrates external knowledge with source-awareness to overcome imperfect retrieval errors in RAG. |
| Outcome: | The proposed approach is superior to previous robustness-enhanced approaches under the worst-case scenario. |
Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling (2025.findings-acl)
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| Challenge: | Conversational assistants are increasingly popular across diverse real-world applications . speech data constitute high-dimensional signals that are difficult to model even for frontier models . |
| Approach: | They propose a data-centric customization approach for enhancing multimodal understanding in conversational speech modeling. |
| Outcome: | The proposed model achieves state-of-the-art on the Spoken-SQuAD benchmark using 10% of training data with open-weight models. |
Universal Self-Adaptive Prompting (2023.emnlp-main)
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| Challenge: | a hallmark of modern large language models is their impressive general zero-shot and few-shot abilities . however, zero- shot performances are weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks. |
| Approach: | They propose an automatic prompt design approach specifically tailored for zero-shot learning that categorizes a possible NLP task into one of three possible task types and then uses a selector to select the most suitable queries and zero- shot model-generated responses as pseudo-demonstrations. |
| Outcome: | The proposed approach is able to generalize ICL to zero-shot learning tasks while also allowing for a more efficient and efficient prompt design. |
Better Zero-Shot Reasoning with Self-Adaptive Prompting (2023.findings-acl)
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| Challenge: | Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. |
| Approach: | They propose a new method that uses a set of examples from the LLM zero-shot outputs to improve performance. |
| Outcome: | The proposed method improves performance up to 15% compared to baselines and matches or exceeds few-shot baselines at a range of reasoning tasks. |
BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models (2024.emnlp-main)
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| Challenge: | Safety backdoors in large language models can be triggered while evading detection during normal interactions. |
| Approach: | They propose a bi-level optimization method that uses a key insight: backdoor triggers induce a uniform drift in the model’s embedding space . inner level identifies universal perturbations to the decoder’s embedded spaces that steer the model towards defender-defined unwanted behaviors; outer level fine-tunes the model to reinforce safe behaviors against these perturbations. |
| Outcome: | The proposed mitigation method reduces the success rate of safety backdoor attacks from over 95% to 1% for general harmful behaviors and from 47% to 0% for Sleeper Agents, without compromising the model’s usefulness. |
ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)
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| Challenge: | Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content . |
| Approach: | They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus. |
| Outcome: | The proposed task is feasible and challenging, and the results are beneficial for future studies. |